Time Reverse Reservoir Localization

ABSTRACT

A method and system for processing synchronous array seismic data includes acquiring synchronous passive seismic data from a plurality of sensors to obtain synchronized array measurements. A reverse-time data process is applied to the synchronized array measurements to obtain a plurality of dynamic particle parameters associated with subsurface locations. These dynamic particle parameters are stored in a form for display. Maximum values of the dynamic particle parameters may be interpreted as reservoir locations. The dynamic particle parameters may be particle displacement values, particle velocity values, particle acceleration values or particle pressure values. The sensors may be three-component sensors. Zero-phase frequency filtering of different ranges of interest may be applied. The data may be resampled to facilitate efficient data processing.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 12/017,527, now U.S. Pat. No. 7,675,815 issued Mar.9, 2010, hereby incorporated by reference in its entirety, which claimsthe benefit of U.S. Provisional Application No. 60/885,887 filed 20 Jan.2007, U.S. Provisional Application No. 60/891,286 filed 23 Feb. 2007 andU.S. Provisional Application No. 60/911,283 filed 12 Apr. 2007.

BACKGROUND OF THE DISCLOSURE

1. Technical Field

The disclosure is related to seismic exploration for oil and gas, andmore particularly to determination of the positions of subsurfacereservoirs.

2. Description

Expensive geophysical and geological exploration investment forhydrocarbons is often focused on acquiring data in the most promisingareas using relatively slow methods, such as reflection seismic dataacquisition and processing. The acquired data are used for mappingpotential hydrocarbon-bearing areas within a survey area to optimizeexploratory or production well locations and to minimize costlynon-productive wells.

The time from mineral discovery to production may be shortened if thetotal time required to evaluate and explore a survey area can be reducedby applying geophysical methods alone or in combination. Some methodsmay be used as a standalone decision tool for oil and gas developmentdecisions when no other data is available.

Geophysical and geological methods are used to maximize production afterreservoir discovery as well. Reservoirs are analyzed using time lapsesurveys (i.e. repeat applications of geophysical methods over time) tounderstand reservoir changes during production. The process of exploringfor and exploiting subsurface hydrocarbon reservoirs is often costly andinefficient because operators have imperfect information fromgeophysical and geological characteristics about reservoir locations.Furthermore, a reservoir's characteristics may change as it is produced.

The impact of oil exploration methods on the environment may be reducedby using low-impact methods and/or by narrowing the scope of methodsrequiring an active source, including reflection seismic andelectromagnetic surveying methods. Various geophysical data acquisitionmethods have a relatively low impact on field survey areas. Low-impactmethods include gravity and magnetic surveys that maybe used to enrichor corroborate structural images and/or integrate with other geophysicaldata, such as reflection seismic data, to delineate hydrocarbon-bearingzones within promising formations and clarify ambiguities in lowerquality data, e.g. where geological or near-surface conditions reducethe effectiveness of reflection seismic methods.

SUMMARY

A method and system for processing synchronous array seismic dataincludes acquiring synchronous passive seismic data from a plurality ofsensors to obtain synchronized array measurements. A reverse-time dataprocess is applied to the synchronized array measurements to obtain aplurality of dynamic particle parameters associated with subsurfacelocations. These dynamic particle parameters are stored in a form fordisplay. Maximum values of the dynamic particle parameters may beinterpreted as reservoir locations. The dynamic particle parameters maybe particle displacement values, particle velocity values, particleacceleration values or particle pressure values. The sensors may bethree-component sensors. Zero-phase frequency filtering of differentranges of interest may be applied. The data may be resampled tofacilitate efficient data processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a method according to anembodiment of the present disclosure for calculating maximum values forsubsurface locations from continuous synchronous signals;

FIG. 2 illustrates various non-limiting possibilities for arrays ofsensor for data acquisition of synchronous signals;

FIG. 3 is a flow chart of reverse-time processing for application toseismic data;

FIG. 4 is a flow chart of a data processing flow that includes acquiringor determining a velocity model associated with reverse-time processingof field data;

FIG. 5 illustrates a model setup for determining a synthetic velocitymodel;

FIG. 6 illustrates snapshots in time of reverse-time and forward-timeprocessing for comparison;

FIG. 7 illustrates maximum values of particle dynamic values plotted fordetermination of reservoir positions;

FIG. 8A illustrates a real input velocity model for reverse-timeprocessing.

FIG. 8B illustrates a synthetic data example with the real velocitymodel of FIG. 8A showing that a reverse simulation of synthetic signalswith source at the location of the assumed reservoir shows that thelocation of the source can be identified very well in this complexmedia.

FIG. 8C illustrates reverse time migration with real field data outputas maximum dynamic particle parameter (in this illustration, velocity)results with a reservoir location; and

FIG. 9 is diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed maycause the machine to perform any one or more of the methods andprocesses described herein.

DETAILED DESCRIPTION

Information to determine the location of hydrocarbon reservoirs may beextracted from naturally occurring seismic waves and vibrations measuredat the earth's surface using passive seismic data acquisition methods.Seismic wave energy emanating from subsurface reservoirs, or otherwisealtered by subsurface reservoirs, is detected by arrays of sensors andthe energy back-propagated with reverse-time processing methods tolocate the source of the energy disturbance. An inversion methodologyfor locating positions of subsurface reservoirs may be based on varioustime reversal processing algorithms of time series measurements ofpassive seismic data.

Passive seismic data acquisition methods rely on seismic energy fromsources not directly associated with the data acquisition. In passiveseismic monitoring there may be no actively controlled and triggeredsource. Examples of sources recorded that may be recorded with passiveseismic acquisition are microseisms (e.g., rhythmically and persistentlyrecurring low-energy earth tremors), microtremors and other ambient orlocalized seismic energy sources.

Microtremors are attributed to the background energy normally present inthe earth. Microtremor seismic waves may include sustained seismicsignals within various or limited frequency ranges. Microtremor signals,like all seismic waves, contain information affecting spectral signaturecharacteristics due to the media or environment that the seismic wavestraverse as well as the source of the seismic energy. These naturallyoccurring and often relatively low frequency background seismic waves(sometimes termed noise or hum) of the earth may be generated from avariety of sources, some of which may be unknown or indeterminate.

Characteristics of microtremor seismic waves in the “infrasonic’ rangemay contain relevant information for direct detection of subsurfaceproperties including the detection of fluid reservoirs. The terminfrasonic may refer to sound waves below the frequencies of soundaudible to humans, and nominally includes frequencies under 20 Hz.

Synchronous arrays of sensors are used to measure vertical andhorizontal components of motion due to background seismic waves atmultiple locations within a survey area. The sensors measure orthogonalcomponents of motion simultaneously.

Local acquisition conditions within a geophysical survey may affectacquired data results. Acquisition conditions impacting acquired signalsmay change over time and may be diurnal. Other acquisition conditionsare related to the near sensor environment. These conditions may beaccounted for during data reduction.

The sensor equipment for measuring seismic waves may be any type ofseismometer for measuring particle dynamics, such as particledisplacements or derivatives of displacements. Seismometer equipmenthaving a large dynamic range and enhanced sensitivity compared withother transducers, particularly in low frequency ranges, may provideoptimum results (e.g., multicomponent earthquake seismometers orequipment with similar capabilities). A number of commercially availablesensors utilizing different technologies may be used, e.g. a balancedforce feed-back instrument or an electrochemical sensor. An instrumentwith high sensitivity at very low frequencies and good coupling with theearth enhances the efficacy of the method.

Noise conditions representative of seismic waves that may have nottraversed or been affected by subsurface reservoirs can negativelyaffect the recorded data. Techniques for removing unwanted noise andartifacts and artificial signals from the data, such as cultural andindustrial noise, are important where ambient noise is relatively highcompared with desired signal energy.

Time-reverse data processing may be used to localize relatively weakseismic events or energy, for example if a reservoir acts as an energysource or significantly affects acoustic energy traversing thereservoir. The seismograms measured at a synchronous array of sensorstations are reversed in time and used as boundary values for thereverse processing. Time-reverse data processing is able to track downevent or energy sources for an S/N-ratio lower than one.

Field surveys have shown that hydrocarbon reservoirs may act as a sourceof low frequency seismic waves and these signals are sometimes termed“hydrocarbon microtremors.” The frequency ranges of microtremors havebeen reported between ˜1 Hz to 6 Hz or greater. A direct and efficientdetection of hydrocarbon reservoirs is of central interest for thedevelopment of new oil or gas fields. One approach is to apply atime-reverse processing/migration. If there is a steady source origin(or other alteration) of low-frequency seismic waves within a reservoir,the location of the reservoir may be located using time reversemigration and may also be used to locate and differentiate stackedreservoirs.

Time reverse processing (or migration) of acquired seismic data, whichmay be in conjunction with modeling, using a grid of nodes is aneffective tool to detect the locality of a steady origin oflow-frequency seismic waves. As a non-limiting example for the purposesof illustration, microtremors may comprise low-frequency signals with afundamental frequency of about 3 Hz and a range between 1.5 Hz and 4.5Hz. Hydrocarbon affected seismic data that include microtremors may havediffering values that are reservoir or case specific. Snapshots (imagesof an inversion representing one or more time steps) showing a currentdynamic particle motion value (e.g., displacement, velocity,acceleration or pressure) at every grid point may be produced atspecific time steps during the reverse-time signal processing. Data fornodes representing high or maximum particle velocity values indicate thelocation of a specific source (or a location related to seismic energysource aberration) of the forward or field acquired data. The maximumvelocities obtained from the reverse-time data processing may be used todelineate parameters associated with the subsurface reservoir location.

There are many known methods for a reverse-time data process for seismicwave field imaging with Earth parameters from inversions of acquiredseismic data. For example, finite-difference, ray-tracing andpseudo-spectral computations, in two- and three-dimensional space, areused for full or partial wave field simulations and imaging of seismicdata. Reverse-time migration algorithms may be based onfinite-difference, ray-tracing or pseudo-spectral wave fieldextrapolators. Output from these reverse-time data processing routinesmay include amplitudes for displacement, velocity, acceleration orpressures values at every time steps of the inversion.

FIG. 1 illustrates a method according to a non-limiting embodiment ofthe present disclosure that includes using passively acquired seismicdata to determine a subsurface location for hydrocarbons or otherreservoir fluids. The embodiment, which may include one or more of thefollowing (in any order), includes acquiring synchronous array seismicdata having a plurality of components 101. The acquired data from eachsensor station may be time stamped and include multiple data vectors. Anexample is passive seismic data, such as multicomponent seismometry datafrom “earthquake” type sensors. The multiple data vectors may each beassociated with an orthogonal direction of movement. Data may beacquired as orthogonal component vectors. The vector data may bearbitrarily mapped or assigned to any coordinate reference system, forexample designated east, north and depth (e.g., respectively, Ve, Vn andVz) or designated V_(x), V_(y) and V_(z) according to any desiredconvention and is amenable to any coordinate system.

Data may be acquired with arrays, which may be 2D or 3D, or evenarbitrarily positioned sensors 201 as illustrated in FIG. 2. FIG. 2illustrates various acquisition geometries which may be selected basedon operational considerations. Array 220 is a 2D array and whileillustrated with regularly spaced sensors 201, regular distribution isnot a requirement. Array 230 and 240 are example illustrations of 3Darrays. Sensor distribution 250 could be considered an array ofarbitrarily placed sensors and may even provide for some modification ofpossible spatial aliasing that can occur with regular spaced sensor 201acquisition arrays.

While data may be acquired with multi-component earthquake seismometerequipment with large dynamic range and enhanced sensitivity, manydifferent types of sensor instruments can be used with differentunderlying technologies and varying sensitivities. Sensor positioningduring recording may vary, e.g. sensors may be positioned on the ground,below the surface or in a borehole. The sensor may be positioned on atripod or rock-pad. Sensors may be enclosed in a protective housing forocean bottom placement. Wherever sensors are positioned, good couplingresults in better data. Recording time may vary, e.g. from minutes tohours or days. In general terms, longer-term measurements may be helpfulin areas where there is high ambient noise and provide extended periodsof data with fewer noise problems.

The layout of a data survey may be varied, e.g. measurement locationsmay be close together or spaced widely apart and different locations maybe occupied for acquiring measurements consecutively or simultaneously.Simultaneous recording of a plurality of locations (a sensor array) mayprovide for relative consistency in environmental conditions that may behelpful in ameliorating problematic or localized ambient noise notrelated to subsurface characteristics of interest. Additionally thearray may provide signal differentiation advantages due to commonalitiesand differences in the recorded signal.

Returning to FIG. 1, the data may be optionally conditioned or cleanedas necessary 103 to account for unwanted noise or signal interference.For example various processing steps such as offset removal, detrendingthe signal and band pass or other targeted frequency filtering. Thevector data may be divided into selected time windows 105 forprocessing. The length of time windows for analysis may be chosen toaccommodate processing or operational concerns.

If a preferred or known range of frequencies for which a hydrocarbonsignature is known or expected, an optional frequency filter (e.g., zerophase, Fourier of other wavelet type) may be applied 107 to conditionthe data for processing. Examples of basis functions for filtering orother processing operations include without limitation the classicFourier transform or one of the many Continuous Wavelet Transforms (CWT)or Discreet Wavelet Transforms. Examples of other transforms includeHaar transforms, Haademard transforms and Wavelet Transforms. The Morletwavelet is an example of a wavelet transform that often may bebeneficially applied to seismic data. Wavelet transforms have theattractive property that the corresponding expansion may bedifferentiable term by term when the seismic trace is smooth.

Additionally, signal analysis, filtering, and suppressing unwantedsignal artifacts may be carried out efficiently using transforms appliedto the acquired data signals. Additionally the data may be resampled 108to facilitate more efficient processing.

The earth velocity model or velocity structure, which may be developedfrom predetermined subsurface velocity information, for use with thereverse-time processing may be input to the work flow at virtually anypoint, but is illustrated here as an example. The velocity model may beresampled to facilitate data processing as well.

Inverting field-acquired passive seismic data to determine the locationof subsurface reservoirs includes using the acquired time-series data as‘sources’ in reverse-time processing 109. The output of the reverse-timeprocessing includes a measure of the dynamic particle motion of sourcesassociated with subsurface positions (which may be nodes of mathematicaldescriptions (i.e., models) of the earth). The maximum values derivedfrom dynamic particle motion, which may be displacements, velocities oraccelerations, may be collected 111 to determine the energy sourcelocation contributing to the dynamics. Plotting the maximum dynamicvalues 113 from all the measurement values output from a reverse-timeprocess may provide a basis for interpreting the location of asubsurface reservoir. The amplitude values associated with subsurfacelocations having the highest relative values may indicate the positionof a reservoir that is the source of hydrocarbon tremors (for exampleFIG. 7). An alternative to checking and storing an updated maximum forevery backward time step is to sum together all the values calculatedfor each time step or subsurface position. The data, whether maximumvalues or summed values, may be contoured or otherwise graphicallydisplayed to illuminate reservoir positions (for example 90 in FIGS. 8 band 92 and 94 in FIG. 8 c).

A non-limiting example of a reverse-time processing inversion isillustrated in FIG. 3 wherein data are input 301 to the processing flow.The data may optionally be filtered to a selected frequency range. Avelocity model for the reverse-time process may be determined from knowninformation 303 or estimated. A wave-equation reverse-time inversion isperformed 305 to obtain particle dynamic behavior 307.

The reverse-time inversion process may include development of an earthmodel that may be based on a priori knowledge or estimates of a surveyarea of interest. During data preparation, the forward modelinginversion may be useful for anticipating and accounting for knownseismic signal or refining the velocity field used for the reverse timeprocessing. Modeling may include accounting for, or the removal of, thenear sensor signal contributions due to environmental field effects,unwanted signal and noise and, thus, the isolation of those parts ofsignals believed to be associated with environmental components beingexamined. By adapting or filtering the data between successiveiterations in the inversion process, predicted signal can be obtained,thus allowing convergence to a structure element indicating whether areservoir is present within the subsurface.

One embodiment for determining reservoir location includes acquiringsynchronous passive seismic data as continuous (digital or analog)signals acquired with arrays of seismometers. Seismic data parametersare determined from the acquired data.

FIG. 4 illustrates an example of a reverse-time process inversion forlocating a reservoir in the subsurface using a velocity model 402 asinput for a reverse-time migration of continuous signals. The reversetime migration may be wave equation based. Any available geosciencesinformation 401 may be used as input to determine parameters for aninitial model 402 that may be modified as input to a reverse-time dataprocess for continuous signals 403 as more information is available ordetermined. Synchronously acquired passive seismic data 405 are input(after any optional processing/conditioning) to the reverse-time dataprocess 403. Particle dynamics such as displacement, velocity oracceleration (or pressure) are determined from the processed data fordetermining dynamic particle behaviour 404. Maximum values may bedetermined 406 and stored 410 to determine subsurface reservoirpositions.

The maximum amplitude values associated with the dynamic particlebehavior, such as velocity values, represent the location of sources ofhydrocarbon tremors. Unlike prior art time-reverse methods, there is nospecific time associated with the source, since the tremor as the sourceis a continuous function unlike discrete seismic events. Not only thetremor source may be located, but noise sources not related to tremorsources may be differentiated as well.

An example of an embodiment illustrated here uses a numerical modelingalgorithm similar to the rotated staggered grid finite-differencetechnique described by Saenger et al. (2000). The two dimensionalnumerical grid is rectangular. Computations may be performed with secondorder spatial explicit finite difference operators and with a secondorder time update. However, as will be well known by practitionersfamiliar with the art, many different reverse-time methods may be usedalong with various wave equation approaches. Extending methods to threedimensions is straightforward.

For one non-limiting illustrative example used herein, a model data setrather than acquired data are input. The grid of the mathematical modelcontains 901 horizontal and 301 vertical nodal points with an intervalof 10 m in both directions. The model setup is similar to the geologicalsituation illustrated in FIG. 5. For simplicity each model unit ishomogeneous and isotropic, though there is no limit on the potentialcomplexity of the situation. There are ten different non-planar sedimentmodel units with P-wave velocities increasing from 1200 m/s (top layer)stepwise by 200 m/s up to 3000 m/s (bottom layer). The velocity isdefined by varying Young's Modulus and a constant density of 2000 kg/m³is applied for all sediment units. The crystalline basement model unitis defined by a density of 3000 kg/m³ and a Young's Modulus of1.08*10¹¹N/m² resulting in a P-wave velocity of 6000 m/s. The lower partof the model is cut by a zone 501 with a density of 2000 kg/m³ and aYoung's Modulus of 8*10⁹N/m² resulting in a P-wave velocity of 2000 m/s.The reservoirs (Reservoir 1 and Reservoir 2) with a thickness of about50 m and a lateral extension of about 2000 m are positioned close to themiddle of the model domain. The reservoirs have a density of 2000 kg/m³and a Young's Modulus of 1.25*10¹° N/m² resulting in a P-wave velocityof 2500 m/s. All S-wave velocities are a multiple of approximately 1.4smaller than the corresponding P-wave velocity.

FIG. 5 consists of ten sediment units and a basement unit. The lowerparts of both models are separated from the tops by a zone with a P-wavevelocity of 2000 m/s. For the top model one reservoir, Reservoir 1,defines seismic source area. The second model includes two source areas(Reservoir 1 and Reservoir 2) as represented by two stacked reservoirs.

A time-reverse inversion may be conducted for the each of the FIG. 5cases, one for the single reservoir case (top model) and anothertime-reverse inversion may be conducted for the two stacked reservoirscase (bottom model). Snapshots as illustrated in FIG. 6 during thereverse processing show an accumulation of high velocities in thevicinity of the sources which were applied in the forward model. Anyapparent inaccuracy near the surface is, as discussed by Gajewski et al.(2005), considerably small because the error of maximal 100 m is muchsmaller than the wavelength of the waves at the central frequency. FIG.6 illustrate the forward model (left column corresponding to field data)and time reverse inversion (right column) for the single reservoirmodel. The top figures show the first steps of both simulations. Thebottom figures correspond to the same time. The microtremors with aknown source from the forward data (such as field acquired data) arevisible in the time reverse (lower right) model.

The area where the highest velocities occur during the reverse modelingdelineates the area in which the point (reservoir) sources of theforward model were distributed is illustrated in FIG. 7. Theaccumulation of high velocities is dense enough to distinguish betweenthe two stacked reservoirs. Microtremor reservoir sources in thesubsurface can be localized with time reverse methods. Both models showa focus of high velocities in the area of the reservoirs, which arezones of microtremor sources in the forward models.

FIG. 8A is illustrative of a velocity structure model for a field areathat in general consists of a low velocity top layer 82, a thickintermediate velocity layer 84 with low velocity contrast 86 relative tothe top layer and a crystalline basement 88 of high velocity. FIG. 8B isillustrative of a reverse simulation of synthetic signals with source atthe location 90 of the assumed reservoir shows that the location of thesource can be identified very well in this complex media. FIG. 8C isillustrative of time reverse modeling with actual field data from thefield area, which are ‘passive’ field measurement data that show apattern similar to the synthetic example with reservoir locations 92 and94.

In one non-limiting embodiment a method and system for processingsynchronous array seismic data includes acquiring synchronous passiveseismic data from a plurality of sensors to obtain synchronized arraymeasurements. A reverse-time data process is applied to the synchronizedarray measurements to obtain a plurality of dynamic particle parametersassociated with subsurface locations. These dynamic particle parametersare stored in a form for display. Maximum values of the dynamic particleparameters may be interpreted as reservoir locations. The dynamicparticle parameters may be particle displacement values, particlevelocity values, particle acceleration values or particle pressurevalues. The sensors may be three-component sensors. Zero-phase frequencyfiltering of different ranges of interest may be applied. The data maybe resampled to facilitate efficient data processing.

FIG. 9 is illustrative of a computing system and operating environmentfor implementing a general purpose computing device in the form of acomputer 10. Computer 10 includes a processing unit 11 that may include‘onboard’ instructions 12. Computer 10 has a system memory 20 attachedto a system bus 40 that operatively couples various system componentsincluding system memory 20 to processing unit 11. The system bus 40 maybe any of several types of bus structures using any of a variety of busarchitectures as are known in the art.

While one processing unit 11 is illustrated in FIG. 9, there may be asingle central-processing unit (CPU) or a graphics processing unit(GPU), or both or a plurality of processing units. Computer 10 may be astandalone computer, a distributed computer, or any other type ofcomputer.

System memory 20 includes read only memory (ROM) 21 with a basicinput/output system (BIOS) 22 containing the basic routines that help totransfer information between elements within the computer 10, such asduring start-up. System memory 20 of computer 10 further includes randomaccess memory (RAM) 23 that may include an operating system (OS) 24, anapplication program 25 and data 26.

Computer 10 may include a disk drive 30 to enable reading from andwriting to an associated computer or machine readable medium 31.Computer readable media 31 includes application programs 32 and programdata 33.

For example, computer readable medium 31 may include programs to processseismic data, which may be stored as program data 33, according to themethods disclosed herein. The application program 32 associated with thecomputer readable medium 31 includes at least one application interfacefor receiving and/or processing program data 33. The program data 33 mayinclude seismic data acquired according to embodiments disclosed herein.At least one application interface may be associated with calculating aratio of data components, which may be spectral components, for locatingsubsurface hydrocarbon reservoirs.

The disk drive may be a hard disk drive for a hard drive (e.g., magneticdisk) or a drive for a magnetic disk drive for reading from or writingto a removable magnetic media, or an optical disk drive for reading fromor writing to a removable optical disk such as a CD ROM, DVD or otheroptical media.

Disk drive 30, whether a hard disk drive, magnetic disk drive or opticaldisk drive is connected to the system bus 40 by a disk drive interface(not shown). The drive 30 and associated computer-readable media 31enable nonvolatile storage and retrieval for application programs 32 anddata 33 that include computer-readable instructions, data structures,program modules and other data for the computer 10. Any type ofcomputer-readable media that can store data accessible by a computer,including but not limited to cassettes, flash memory, digital videodisks in all formats, random access memories (RAMs), read only memories(ROMs), may be used in a computer 10 operating environment.

Data input and output devices may be connected to the processing unit 11through a serial interface 50 that is coupled to the system bus. Serialinterface 50 may a universal serial bus (USB). A user may enter commandsor data into computer 10 through input devices connected to serialinterface 50 such as a keyboard 53 and pointing device (mouse) 52. Otherperipheral input/output devices 54 may include without limitation amicrophone, joystick, game pad, satellite dish, scanner or fax,speakers, wireless transducer, etc. Other interfaces (not shown) thatmay be connected to bus 40 to enable input/output to computer 10 includea parallel port or a game port. Computers often include other peripheralinput/output devices 54 that may be connected with serial interface 50such as a machine readable media 55 (e.g., a memory stick), a printer 56and a data sensor 57. A seismic sensor or seismometer for practicingembodiments disclosed herein is a nonlimiting example of data sensor 57.A video display 72 (e.g., a liquid crystal display (LCD), a flat panel,a solid state display, or a cathode ray tube (CRT)) or other type ofoutput display device may also be connected to the system bus 40 via aninterface, such as a video adapter 70. A map display created fromspectral ratio values as disclosed herein may be displayed with videodisplay 72.

A computer 10 may operate in a networked environment using logicalconnections to one or more remote computers. These logical connectionsare achieved by a communication device associated with computer 10. Aremote computer may be another computer, a server, a router, a networkcomputer, a workstation, a client, a peer device or other common networknode, and typically includes many or all of the elements describedrelative to computer 10. The logical connections depicted in FIG. 9include a local-area network (LAN) or a wide-area network (WAN) 90.However, the designation of such networking environments, whether LAN orWAN, is often arbitrary as the functionalities may be substantiallysimilar. These networks are common in offices, enterprise-wide computernetworks, intranets and the Internet.

When used in a networking environment, the computer 10 may be connectedto a network 90 through a network interface or adapter 60. Alternativelycomputer 10 may include a modem 51 or any other type of communicationsdevice for establishing communications over the network 90, such as theInternet. Modem 51, which may be internal or external, may be connectedto the system bus 40 via the serial interface 50.

In a networked deployment computer 10 may operate in the capacity of aserver or a client user machine in server-client user networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In a networked environment, program modulesassociated with computer 10, or portions thereof, may be stored in aremote memory storage device. The network connections schematicallyillustrated are for example only and other communications devices forestablishing a communications link between computers may be used.

While various embodiments have been shown and described, variousmodifications and substitutions may be made thereto without departingfrom the spirit and scope of the disclosure herein. Accordingly, it isto be understood that the present embodiments have been described by wayof illustration and not limitation.

1-20. (canceled)
 21. A method for processing synchronous array seismicdata comprising: a) acquiring seismic data from a plurality of sensorsto obtain synchronized array measurements; b) selecting seismic datawithout reference to phase information of the seismic data; c) applyinga reverse-time data process using a processing unit to the synchronizedarray measurements to obtain a dynamic particle parameter associatedwith each of a plurality of subsurface locations; and d) storing thevalue of the obtained dynamic particle parameter associated with each ofthe plurality of subsurface locations only if the dynamic particleparameter value is greater than any previous value associated with thesubsurface location for the reverse-time data process.
 22. The method ofclaim 21 wherein the stored value is stored in a form for display. 23.The method of claim 21 wherein the plurality of dynamic particleparameters are at least one selected from the group consisting of i)particle velocity values, ii) particle acceleration values and iii)particle pressure values.
 24. The method of claim 21 wherein theplurality of sensors are three-component sensors.
 25. The method ofclaim 21 further comprising applying a zero-phase frequency filter tothe synchronized array measurements.
 26. The method of claim 21 furthercomprising resampling the synchronized array measurements.
 27. Themethod of claim 21 further comprising an extrapolator selected from agroup consisting of i) finite-difference reverse time migration, ii)ray-tracing reverse time migration and iii) pseudo-spectral reverse timemigration.
 28. A method for processing synchronous array seismic datacomprising: a) acquiring seismic data from a plurality of sensors toobtain synchronized array measurements; b) selecting seismic datawithout reference to phase information of the seismic data; c) applyinga reverse-time data process using a processing unit to the synchronizedarray measurements to obtain a dynamic particle parameter associatedwith each of a plurality of subsurface locations; and d) summing valuesfor every obtained dynamic particle parameter associated with each ofthe plurality of subsurface locations to obtain a summed data valueassociated with each of the plurality of subsurface locations.
 29. Themethod of claim 28 wherein the summed data value of dynamic particleparameters is stored in a form for display.
 30. The method of claim 28wherein the plurality of dynamic particle parameters are at least oneselected from the group consisting of i) particle velocity values, ii)particle acceleration values and iii) particle pressure values.
 31. Themethod of claim 28 wherein the plurality of sensors are three-componentsensors.
 32. The method of claim 28 further comprising applying azero-phase frequency filter to the synchronized array measurements. 33.The method of claim 28 further comprising resampling the synchronizedarray measurements.
 34. The method of claim 28 further comprising anextrapolator selected from a group consisting of i) finite-differencereverse time migration, ii) ray-tracing reverse time migration and iii)pseudo-spectral reverse time migration.